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File size: 28,574 Bytes
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---
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- name: cpu_prediction_time
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- name: memory_usage
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- name: learning_rate
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- config_name: ltfsid
features:
- name: instance
dtype: int64
- name: Area
dtype: int64
- name: Sensing Range
dtype: int64
- name: Transmission Range
dtype: int64
- name: Number of Sensor nodes
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- name: real
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- name: memory_usage
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- name: validation
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- name: test
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num_examples: 2805
download_size: 222167
dataset_size: 1722600
- config_name: music_popularity
features:
- name: instance
dtype: int64
- name: acousticness
dtype: float64
- name: danceability
dtype: float64
- name: duration_ms
dtype: int64
- name: energy
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- name: explicit
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- name: instrumentalness
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- name: key
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- name: liveness
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- name: loudness
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- name: mode
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- name: speechiness
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- name: tempo
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- name: valence
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- name: year
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- name: real
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- name: model
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- name: memory_usage
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- config_name: parkinsons_total
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- name: age
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- name: ShimmerAPQ11
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- name: sex
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- name: cpu_prediction_time
dtype: int64
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- name: validation
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num_examples: 89760
download_size: 54936377
dataset_size: 109164960
- config_name: swCSC
features:
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- name: FEh
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dtype: bool
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dtype: string
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- name: memory_usage
dtype: int64
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dtype: int64
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num_examples: 2040
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dataset_size: 1211187
configs:
- config_name: abalone
data_files:
- split: train
path: abalone/train-*
- split: validation
path: abalone/validation-*
- split: test
path: abalone/test-*
- config_name: auction_verification
data_files:
- split: train
path: auction_verification/train-*
- split: validation
path: auction_verification/validation-*
- split: test
path: auction_verification/test-*
- config_name: bng_echoMonths
data_files:
- split: train
path: bng_echoMonths/train-*
- split: validation
path: bng_echoMonths/validation-*
- split: test
path: bng_echoMonths/test-*
- config_name: california_housing
data_files:
- split: train
path: california_housing/train-*
- split: validation
path: california_housing/validation-*
- split: test
path: california_housing/test-*
- config_name: infrared
data_files:
- split: train
path: infrared/train-*
- split: validation
path: infrared/validation-*
- split: test
path: infrared/test-*
- config_name: life_expectancy
data_files:
- split: train
path: life_expectancy/train-*
- split: validation
path: life_expectancy/validation-*
- split: test
path: life_expectancy/test-*
- config_name: ltfsid
data_files:
- split: train
path: ltfsid/train-*
- split: validation
path: ltfsid/validation-*
- split: test
path: ltfsid/test-*
- config_name: music_popularity
data_files:
- split: train
path: music_popularity/train-*
- split: validation
path: music_popularity/validation-*
- split: test
path: music_popularity/test-*
- config_name: parkinsons_motor
data_files:
- split: train
path: parkinsons_motor/train-*
- split: validation
path: parkinsons_motor/validation-*
- split: test
path: parkinsons_motor/test-*
- config_name: parkinsons_total
data_files:
- split: train
path: parkinsons_total/train-*
- split: validation
path: parkinsons_total/validation-*
- split: test
path: parkinsons_total/test-*
- config_name: swCSC
data_files:
- split: train
path: swCSC/train-*
- split: validation
path: swCSC/validation-*
- split: test
path: swCSC/test-*
task_categories:
- tabular-regression
modalities:
- tabular
---
# Assessors For Regression: Loss Analysis - Instance Level Results
AFRLA - Instance Level Results is a collection of predictions at the instance level for eleven different regression tasks tested on 255 tree-based models. The aim of this dataset is to provide example-level results to train assessor models to predict performance of the tree-based models.
## The dataset
The dataset presents eleven sections (one per regression task), with varying degrees of performance, difficulty and characteristics from the original tasks. Every one of the 255 models was trained on a subset of the dataset used for every task, and the results shown here are the test (never-before-seen by the models) predictions. Each subset has:
- An **instance identifier** indicating the instance nº from the test set. This is just an identifier and it is not usually employed for training assessors, although in some occasions it may be useful.
- The **original task features**, the features used by the models to learn the task. Along with the instance identifier, they fully describe a test example.
- The **model features**, descriptors of the 255 models. Mainly:
- The model used (XGBoost, Random Forest, Decision Tree...)
- Hyperparameters such as the maximum depth, number of estimators if applicable...
- Profiling metrics such as training time, inference time or memory usage
These metrics are not recorded per example, but rather per model (that is, if the inference time is 1.2 ms, the model predicted *the entirety of the test dataset* in that time, instead of just that example), and are then casted for each example. As such, they fully describre a model.
## Partitions and versions
The sections are already partitioned into a predefined train-validation-test split for training assessors. Assessors need a particular kind of partitioning (mainly stratified by instance identifier to avoid contamination), so that's why the subsets are given.
The **main** branch contains the unaltered datasets, keeping the original values of the task and model characteristics, whereas the **normalised** branch contains the datasets properly normalised.
## Original tasks
| **Dataset** | **#Feat.** | **#Inst.** | **Cat.** | **Num.** | **Domain** |
|--------------------------------------|------------|------------|----------|----------|------------|
| Abalone | 8 | 4177 | Yes | Yes | Biology |
| Auction Verification | 8 | 2043 | Yes | Yes | Commerce |
| BGN EchoMonts | 10 | 17496 | Yes | Yes | Health |
| California Housing | 8 | 20640 | Yes | Yes | Real State |
| Infrared Thermography Temperature | 33 | 1020 | Yes | Yes | Health |
| Life Expectancy | 21 | 2938 | Yes | Yes | Health |
| Music Popularity | 14 | 43597 | Yes | Yes | Music |
| Parkinsons Telemonitoring (*motor*) | 20 | 5875 | No | Yes | Health |
| Parkinsons Telemonitoring (*total*) | 20 | 5875 | No | Yes | Health |
| Software Cost Estimation | 6 | 145 | Yes | Yes | Projects |
|